Research Papers: Gas Turbines: Combustion, Fuels, and Emissions

Fuel-Sensitive Ignition Delay Models for a Local and Global Description of Direct Injection Internal Combustion Engines

[+] Author and Article Information
Kyoung Hyun Kwak

Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: khkwak@umich.edu

Claus Borgnakke

Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: claus@umich.edu

Dohoy Jung

Department of Mechanical Engineering,
University of Michigan-Dearborn,
Dearborn, MI 48128
e-mail: dohoy@umich.edu

1Corresponding author.

Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received March 12, 2015; final manuscript received March 24, 2015; published online May 12, 2015. Editor: David Wisler.

J. Eng. Gas Turbines Power 137(11), 111510 (Nov 01, 2015) (9 pages) Paper No: GTP-15-1091; doi: 10.1115/1.4030293 History: Received March 12, 2015; Revised March 24, 2015; Online May 12, 2015

Models for ignition delay are investigated and fuel-specific properties are included to predict the effects of different fuels on the ignition delay. These models follow the Arrhenius type expression for the ignition delay modified with the oxygen concentration and Cetane number to extend the range of validity. In this investigation, two fuel-sensitive spray ignition delay models are developed: a global model and a local model. The global model is based on the global combustion chamber charge properties including temperature, pressure, and oxygen/fuel content. The local model is developed to account for temporal and spatial variations in properties of separated spray zones such as local temperature, oxidizer, and fuel concentrations obtained by a quasi-dimensional multizone fuel spray model. These variations are integrated in time to predict the ignition delay. Often ignition delay models are recalibrated for a specific fuel but in this study, the global ignition delay model includes the Cetane number to capture ignition delay of various fuels. The local model uses Cetane number and local stoichiometric oxygen to fuel molar ratio. The model is therefore capable of predicting spray ignition delays for a set of fuels with a single calibration. Experimental dataset of spray ignition delay in a constant volume chamber is used for model development and calibration. The models show a good accuracy for the predicted ignition delay of four different fuels: JP8, DF2, n-heptane, and n-dodecane. The investigation revealed that the most accurate form of the models is from a calibration done for each individual fuel with only a slight decrease in accuracy when a single calibration is done for all fuels. The single calibration case is the more desirable outcome as it leads to general models that cover all the fuels. Of the two proposed models, the local model has a slightly better accuracy compared to the global model. Results for both models demonstrate the improvements that can be obtained for the ignition delay model when additional fuel-specific properties are included in the spray ignition model. Other alternative fuels like synthetic oxygenated fuels were included in the investigation. These fuels behave differently such that the Cetane number does not provide the same explanation for the trend in ignition delay. Though of lower accuracy, the new models do improve the predictive capability when compared with existing types of ignition delay models applied to this kind of fuels.

Copyright © 2015 by ASME
Topics: Fuels , Sprays , Delays , Ignition
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Fig. 1

Comparison of n-heptane ignition of spray in constant volume chamber versus shock tube ignition. Spray ignition delay data are obtained from ECN database [25], and shock tube data are obtained from Ciezki and Adomeit [26]. Presented data are scaled to 50 atm with pressure exponent −1.

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Fig. 2

Comparison of activation temperatures: (a) JP8, (b) DF2, (c) n-heptane, and (d) n-dodecane

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Fig. 3

Monotonic behavior of pre-exponential parameter Ag when θg is 3171 K and its curve fitting model R2= 0.9829

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Fig. 4

Schematic representation of multizone spray concept

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Fig. 5

Effect of different values of concentration exponents

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Fig. 6

Regression of pre-exponent parameter Az versus Cetane number for local ignition delay model

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Fig. 7

Prediction result of fuel-sensitive spray ignition delay model using global information

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Fig. 8

Prediction result of fuel-sensitive spray ignition delay model using local spray information

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Fig. 9

Prediction result of calibrated Wolfer’s ignition delay model

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Fig. 10

Prediction result of Aligrot’s ignition delay model

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Fig. 11

Prediction result of calibrated Rakopoulos’ ignition delay model

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Fig. 12

Prediction result of calibrated Zheng’s ignition delay model

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Fig. 13

Result of global ignition delay correlation with T70, CN80, GE80, and BM88

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Fig. 14

Result by local ignition delay with T70, CN80, GE80, and BM88

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Fig. 15

Prediction result of Wolfer’s ignition delay with T70, CN80, GE80, and BM88

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Fig. 16

Prediction result of Aligrot’s ignition delay model with T70, CN80, GE80, and BM88

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Fig. 17

Prediction result of calibrated Rakopoulos’ ignition delay model with T70, CN80, GE80, and BM88

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Fig. 18

Prediction result of calibrated Zheng’s ignition delay model with T70, CN80, GE80, and BM88



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